v0.11.1
Highlights
This release includes 1456 commits from 449 contributors (184 new contributors)!
Key changes include:
- PyTorch 2.9.0 + CUDA 12.9.1: Updated the default CUDA build to
torch==2.9.0+cu129, enabling Inductor partitioning and landing multiple fixes in graph-partition rules and compile-cache integration. - Batch-invariant
torch.compile: Generalized batch-invariant support across attention and MoE backends, with explicit support for DeepGEMM and FlashInfer on Hopper and Blackwell GPUs. - Robust async scheduling: Fixed several correctness and stability issues in async scheduling, especially when combined with chunked prefill, structured outputs, priority scheduling, MTP, and DeepEP / DCP. We expect
--async-schedulingto be enabled by default in the next release. - Stronger scheduler + KV ecosystem: Improved test coverage in CI and made scheduler behavior more robust with KV connectors, prefix caching, and multi-node deployments.
- Anthropic API Support: Added support for the
/v1/messagesendpoint, allowing users to interact with using Anthropic-compatible clients.